def test_field_add_remove_metadata():
    import collections

    f0 = pa.field('foo', pa.int32())

    assert f0.metadata is None

    metadata = {b'foo': b'bar', b'pandas': b'badger'}
    metadata2 = collections.OrderedDict([
        (b'a', b'alpha'),
        (b'b', b'beta')
    ])

    f1 = f0.add_metadata(metadata)
    assert f1.metadata == metadata

    f2 = f0.add_metadata(metadata2)
    assert f2.metadata == metadata2

    with pytest.raises(TypeError):
        f0.add_metadata([1, 2, 3])

    f3 = f1.remove_metadata()
    assert f3.metadata is None

    # idempotent
    f4 = f3.remove_metadata()
    assert f4.metadata is None

    f5 = pa.field('foo', pa.int32(), True, metadata)
    f6 = f0.add_metadata(metadata)
    assert f5.equals(f6)

def test_struct_type():
    fields = [pa.field('a', pa.int64()),
              pa.field('a', pa.int32()),
              pa.field('b', pa.int32())]
    ty = pa.struct(fields)

    assert len(ty) == ty.num_children == 3
    assert list(ty) == fields

    for a, b in zip(ty, fields):
        a == b

    # Construct from list of tuples
    ty = pa.struct([('a', pa.int64()),
                    ('a', pa.int32()),
                    ('b', pa.int32())])
    assert list(ty) == fields
    for a, b in zip(ty, fields):
        a == b

    # Construct from mapping
    fields = [pa.field('a', pa.int64()),
              pa.field('b', pa.int32())]
    ty = pa.struct(OrderedDict([('a', pa.int64()),
                                ('b', pa.int32())]))
    assert list(ty) == fields
    for a, b in zip(ty, fields):
        a == b

def test_schema():
    fields = [
        pa.field('foo', pa.int32()),
        pa.field('bar', pa.string()),
        pa.field('baz', pa.list_(pa.int8()))
    ]
    sch = pa.schema(fields)

    assert sch.names == ['foo', 'bar', 'baz']
    assert sch.types == [pa.int32(), pa.string(), pa.list_(pa.int8())]

    assert len(sch) == 3
    assert sch[0].name == 'foo'
    assert sch[0].type == fields[0].type
    assert sch.field_by_name('foo').name == 'foo'
    assert sch.field_by_name('foo').type == fields[0].type

    assert repr(sch) == """\
foo: int32
bar: string
baz: list<item: int8>
  child 0, item: int8"""

    with pytest.raises(TypeError):
        pa.schema([None])

def test_is_union():
    for mode in [pa.lib.UnionMode_SPARSE, pa.lib.UnionMode_DENSE]:
        assert types.is_union(pa.union([pa.field('a', pa.int32()),
                                        pa.field('b', pa.int8()),
                                        pa.field('c', pa.string())],
                                       mode=mode))
    assert not types.is_union(pa.list_(pa.int32()))

    def test_timestamps_notimezone_nulls(self):
        df = pd.DataFrame({
            'datetime64': np.array([
                '2007-07-13T01:23:34.123',
                None,
                '2010-08-13T05:46:57.437'],
                dtype='datetime64[ms]')
            })
        field = pa.field('datetime64', pa.timestamp('ms'))
        schema = pa.schema([field])
        self._check_pandas_roundtrip(
            df,
            timestamps_to_ms=True,
            expected_schema=schema,
        )

        df = pd.DataFrame({
            'datetime64': np.array([
                '2007-07-13T01:23:34.123456789',
                None,
                '2010-08-13T05:46:57.437699912'],
                dtype='datetime64[ns]')
            })
        field = pa.field('datetime64', pa.timestamp('ns'))
        schema = pa.schema([field])
        self._check_pandas_roundtrip(
            df,
            timestamps_to_ms=False,
            expected_schema=schema,
        )

def test_table_safe_casting():
    data = [
        pa.array(range(5), type=pa.int64()),
        pa.array([-10, -5, 0, 5, 10], type=pa.int32()),
        pa.array([1.0, 2.0, 3.0, 4.0, 5.0], type=pa.float64()),
        pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string())
    ]
    table = pa.Table.from_arrays(data, names=tuple('abcd'))

    expected_data = [
        pa.array(range(5), type=pa.int32()),
        pa.array([-10, -5, 0, 5, 10], type=pa.int16()),
        pa.array([1, 2, 3, 4, 5], type=pa.int64()),
        pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string())
    ]
    expected_table = pa.Table.from_arrays(expected_data, names=tuple('abcd'))

    target_schema = pa.schema([
        pa.field('a', pa.int32()),
        pa.field('b', pa.int16()),
        pa.field('c', pa.int64()),
        pa.field('d', pa.string())
    ])
    casted_table = table.cast(target_schema)

    assert casted_table.equals(expected_table)

def test_table_unsafe_casting():
    data = [
        pa.array(range(5), type=pa.int64()),
        pa.array([-10, -5, 0, 5, 10], type=pa.int32()),
        pa.array([1.1, 2.2, 3.3, 4.4, 5.5], type=pa.float64()),
        pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string())
    ]
    table = pa.Table.from_arrays(data, names=tuple('abcd'))

    expected_data = [
        pa.array(range(5), type=pa.int32()),
        pa.array([-10, -5, 0, 5, 10], type=pa.int16()),
        pa.array([1, 2, 3, 4, 5], type=pa.int64()),
        pa.array(['ab', 'bc', 'cd', 'de', 'ef'], type=pa.string())
    ]
    expected_table = pa.Table.from_arrays(expected_data, names=tuple('abcd'))

    target_schema = pa.schema([
        pa.field('a', pa.int32()),
        pa.field('b', pa.int16()),
        pa.field('c', pa.int64()),
        pa.field('d', pa.string())
    ])

    with pytest.raises(pa.ArrowInvalid,
                       match='Floating point value truncated'):
        table.cast(target_schema)

    casted_table = table.cast(target_schema, safe=False)
    assert casted_table.equals(expected_table)

def test_struct_array_slice():
    # ARROW-2311: slicing nested arrays needs special care
    ty = pa.struct([pa.field('a', pa.int8()),
                    pa.field('b', pa.float32())])
    arr = pa.array([(1, 2.5), (3, 4.5), (5, 6.5)], type=ty)
    assert arr[1:].to_pylist() == [{'a': 3, 'b': 4.5},
                                   {'a': 5, 'b': 6.5}]

def test_struct_array_field():
    ty = pa.struct([pa.field('x', pa.int16()),
                    pa.field('y', pa.float32())])
    a = pa.array([(1, 2.5), (3, 4.5), (5, 6.5)], type=ty)

    x0 = a.field(0)
    y0 = a.field(1)
    x1 = a.field(-2)
    y1 = a.field(-1)
    x2 = a.field('x')
    y2 = a.field('y')

    assert isinstance(x0, pa.lib.Int16Array)
    assert isinstance(y1, pa.lib.FloatArray)
    assert x0.equals(pa.array([1, 3, 5], type=pa.int16()))
    assert y0.equals(pa.array([2.5, 4.5, 6.5], type=pa.float32()))
    assert x0.equals(x1)
    assert x0.equals(x2)
    assert y0.equals(y1)
    assert y0.equals(y2)

    for invalid_index in [None, pa.int16()]:
        with pytest.raises(TypeError):
            a.field(invalid_index)

    for invalid_index in [3, -3]:
        with pytest.raises(IndexError):
            a.field(invalid_index)

    for invalid_name in ['z', '']:
        with pytest.raises(KeyError):
            a.field(invalid_name)

def test_buffers_nested():
    a = pa.array([[1, 2], None, [3, None, 4, 5]], type=pa.list_(pa.int64()))
    buffers = a.buffers()
    assert len(buffers) == 4
    # The parent buffers
    null_bitmap = buffers[0].to_pybytes()
    assert bytearray(null_bitmap)[0] == 0b00000101
    offsets = buffers[1].to_pybytes()
    assert struct.unpack('4i', offsets) == (0, 2, 2, 6)
    # The child buffers
    null_bitmap = buffers[2].to_pybytes()
    assert bytearray(null_bitmap)[0] == 0b00110111
    values = buffers[3].to_pybytes()
    assert struct.unpack('qqq8xqq', values) == (1, 2, 3, 4, 5)

    a = pa.array([(42, None), None, (None, 43)],
                 type=pa.struct([pa.field('a', pa.int8()),
                                 pa.field('b', pa.int16())]))
    buffers = a.buffers()
    assert len(buffers) == 5
    # The parent buffer
    null_bitmap = buffers[0].to_pybytes()
    assert bytearray(null_bitmap)[0] == 0b00000101
    # The child buffers: 'a'
    null_bitmap = buffers[1].to_pybytes()
    assert bytearray(null_bitmap)[0] == 0b00000001
    values = buffers[2].to_pybytes()
    assert struct.unpack('bxx', values) == (42,)
    # The child buffers: 'b'
    null_bitmap = buffers[3].to_pybytes()
    assert bytearray(null_bitmap)[0] == 0b00000100
    values = buffers[4].to_pybytes()
    assert struct.unpack('4xh', values) == (43,)

def test_recordbatch_basics():
    data = [
        pa.array(range(5)),
        pa.array([-10, -5, 0, 5, 10])
    ]

    batch = pa.RecordBatch.from_arrays(data, ['c0', 'c1'])
    assert not batch.schema.metadata

    assert len(batch) == 5
    assert batch.num_rows == 5
    assert batch.num_columns == len(data)
    assert batch.to_pydict() == OrderedDict([
        ('c0', [0, 1, 2, 3, 4]),
        ('c1', [-10, -5, 0, 5, 10])
    ])

    with pytest.raises(IndexError):
        # bounds checking
        batch[2]

    # Schema passed explicitly
    schema = pa.schema([pa.field('c0', pa.int16()),
                        pa.field('c1', pa.int32())],
                       metadata={b'foo': b'bar'})
    batch = pa.RecordBatch.from_arrays(data, schema)
    assert batch.schema == schema

def test_type_schema_pickling():
    cases = [
        pa.int8(),
        pa.string(),
        pa.binary(),
        pa.binary(10),
        pa.list_(pa.string()),
        pa.struct([
            pa.field('a', 'int8'),
            pa.field('b', 'string')
        ]),
        pa.time32('s'),
        pa.time64('us'),
        pa.date32(),
        pa.date64(),
        pa.timestamp('ms'),
        pa.timestamp('ns'),
        pa.decimal(12, 2),
        pa.field('a', 'string', metadata={b'foo': b'bar'})
    ]

    for val in cases:
        roundtripped = pickle.loads(pickle.dumps(val))
        assert val == roundtripped

    fields = []
    for i, f in enumerate(cases):
        if isinstance(f, pa.Field):
            fields.append(f)
        else:
            fields.append(pa.field('_f{}'.format(i), f))

    schema = pa.schema(fields, metadata={b'foo': b'bar'})
    roundtripped = pickle.loads(pickle.dumps(schema))
    assert schema == roundtripped

def make_recordbatch(length):
    schema = pa.schema([pa.field('f0', pa.int16()),
                        pa.field('f1', pa.int16())])
    a0 = pa.array(np.random.randint(0, 255, size=length, dtype=np.int16))
    a1 = pa.array(np.random.randint(0, 255, size=length, dtype=np.int16))
    batch = pa.RecordBatch.from_arrays([a0, a1], schema)
    return batch

def test_struct_from_tuples():
    ty = pa.struct([pa.field('a', pa.int32()),
                    pa.field('b', pa.string()),
                    pa.field('c', pa.bool_())])

    data = [(5, 'foo', True),
            (6, 'bar', False)]
    expected = [{'a': 5, 'b': 'foo', 'c': True},
                {'a': 6, 'b': 'bar', 'c': False}]
    arr = pa.array(data, type=ty)

    data_as_ndarray = np.empty(len(data), dtype=object)
    data_as_ndarray[:] = data
    arr2 = pa.array(data_as_ndarray, type=ty)
    assert arr.to_pylist() == expected

    assert arr.equals(arr2)

    # With omitted values
    data = [(5, 'foo', None),
            None,
            (6, None, False)]
    expected = [{'a': 5, 'b': 'foo', 'c': None},
                None,
                {'a': 6, 'b': None, 'c': False}]
    arr = pa.array(data, type=ty)
    assert arr.to_pylist() == expected

    # Invalid tuple size
    for tup in [(5, 'foo'), (), ('5', 'foo', True, None)]:
        with pytest.raises(ValueError, match="(?i)tuple size"):
            pa.array([tup], type=ty)

def test_field_metadata():
    f1 = pa.field('a', pa.int8())
    f2 = pa.field('a', pa.int8(), metadata={})
    f3 = pa.field('a', pa.int8(), metadata={b'bizz': b'bazz'})

    assert f1.metadata is None
    assert f2.metadata == {}
    assert f3.metadata[b'bizz'] == b'bazz'

def test_struct_from_mixed_sequence():
    # It is forbidden to mix dicts and tuples when initializing a struct array
    ty = pa.struct([pa.field('a', pa.int32()),
                    pa.field('b', pa.string()),
                    pa.field('c', pa.bool_())])
    data = [(5, 'foo', True),
            {'a': 6, 'b': 'bar', 'c': False}]
    with pytest.raises(TypeError):
        pa.array(data, type=ty)

def test_field_equality_operators():
    f1 = pa.field('a', pa.int8(), nullable=True)
    f2 = pa.field('a', pa.int8(), nullable=True)
    f3 = pa.field('b', pa.int8(), nullable=True)
    f4 = pa.field('b', pa.int8(), nullable=False)

    assert f1 == f2
    assert f1 != f3
    assert f3 != f4
    assert f1 != 'foo'

def test_fields_hashable():
    in_dict = {}
    fields = [pa.field('a', pa.int64()),
              pa.field('a', pa.int32()),
              pa.field('b', pa.int32())]
    for i, field in enumerate(fields):
        in_dict[field] = i
    assert len(in_dict) == len(fields)
    for i, field in enumerate(fields):
        assert in_dict[field] == i

def dataframe_with_arrays(include_index=False):
    """
    Dataframe with numpy arrays columns of every possible primtive type.

    Returns
    -------
    df: pandas.DataFrame
    schema: pyarrow.Schema
        Arrow schema definition that is in line with the constructed df.
    """
    dtypes = [('i1', pa.int8()), ('i2', pa.int16()),
              ('i4', pa.int32()), ('i8', pa.int64()),
              ('u1', pa.uint8()), ('u2', pa.uint16()),
              ('u4', pa.uint32()), ('u8', pa.uint64()),
              ('f4', pa.float32()), ('f8', pa.float64())]

    arrays = OrderedDict()
    fields = []
    for dtype, arrow_dtype in dtypes:
        fields.append(pa.field(dtype, pa.list_(arrow_dtype)))
        arrays[dtype] = [
            np.arange(10, dtype=dtype),
            np.arange(5, dtype=dtype),
            None,
            np.arange(1, dtype=dtype)
        ]

    fields.append(pa.field('str', pa.list_(pa.string())))
    arrays['str'] = [
        np.array([u"1", u"ä"], dtype="object"),
        None,
        np.array([u"1"], dtype="object"),
        np.array([u"1", u"2", u"3"], dtype="object")
    ]

    fields.append(pa.field('datetime64', pa.list_(pa.timestamp('ms'))))
    arrays['datetime64'] = [
        np.array(['2007-07-13T01:23:34.123456789',
                  None,
                  '2010-08-13T05:46:57.437699912'],
                 dtype='datetime64[ms]'),
        None,
        None,
        np.array(['2007-07-13T02',
                  None,
                  '2010-08-13T05:46:57.437699912'],
                 dtype='datetime64[ms]'),
    ]

    if include_index:
        fields.append(pa.field('__index_level_0__', pa.int64()))
    df = pd.DataFrame(arrays)
    schema = pa.schema(fields)

    return df, schema

def test_field():
    t = pa.string()
    f = pa.field('foo', t)

    assert f.name == 'foo'
    assert f.nullable
    assert f.type is t
    assert repr(f) == "pyarrow.Field<foo: string>"

    f = pa.field('foo', t, False)
    assert not f.nullable